Distributed Fault Diagnosis Research Articles

Multiscale Spatial–Temporal Bayesian Graph Conv-Transformer-Based Distributed Fault Diagnosis for UAVs Swarm System

Multiscale Spatial–Temporal Bayesian Graph Conv-Transformer-Based Distributed Fault Diagnosis for UAVs Swarm System

Federated transfer learning-based distributed fault diagnosis method for rolling bearings

Abstract Current methods for bearing fault diagnosis often fall short in addressing data privacy concerns and typically rely on one-to-one transfer strategies, which are inadequate for achieving knowledge transfer in distributed environments. To address this issue, a distributed fault diagnosis method for rolling bearings based on federated transfer learning is proposed. This method ensures data privacy while integrating fault knowledge from multiple domains, thereby enabling more efficient knowledge transfer. Specifically, a Domain Adversarial Neural Network (DANN) is introduced as the base model within the federated learning framework. Additionally, Maximum Mean Discrepancy (MMD) is incorporated into the DANN to enhance the transfer of fault knowledge. Finally, a dynamic weighting parameter update method based on MMD is designed to evaluate the feature discrepancies between source and target domains, thereby updating the parameters of the federated framework and achieving global model aggregation. Experimental results on two bearing datasets demonstrate that the proposed method excels in both distribution alignment and fault diagnosis.

Distributed Fault Diagnosis for Heterogeneous Multiagent Systems: A Hybrid Knowledge-Based and Data-Driven Method

Distributed Fault Diagnosis for Heterogeneous Multiagent Systems: A Hybrid Knowledge-Based and Data-Driven Method

Distributed Fault Diagnosis in Discrete Event Systems With Transmission Delay Impairments

Distributed Fault Diagnosis in Discrete Event Systems With Transmission Delay Impairments

An Adjustable Parameter-Based Robust Distributed Fault Diagnosis for One-Sided Lipschitz Formation of Clustered Multi-Agent Systems

This paper addresses the challenge of distributed fault diagnosis in the context of the one-sided Lipschitz formation of agents. Each agent integrates an observer to detect and estimate both linear and non-linear faults in its attitude control subsystem. A robust design configuration is also developed to account for external perturbations. The robust observer utilized in this study is an unknown input observer (UIO), designed to mitigate the impact of disturbances on fault and state estimation errors. The observer’s parameters are determined using linear matrix inequalities (LMIs). Furthermore, a UIO incorporating an adjustable parameter (AP) is introduced to enhance fault diagnosis accuracy. Simulation results for two satellite clusters, consisting of five satellites with varying dynamics due to external disturbances, are presented to validate the approach. Instead of equipping every agent with an observer, specific agents can be equipped with observers to detect faults throughout the constellation, thereby reducing computational demands in configurations with numerous agents. Finally, a comparison is made between the proposed AP-based UIO and a standard UIO. The comparison findings reveal a noteworthy average of a substantial 56.61% reduction in root mean square error (RMSE) employing AP-based UIO compared to the utilization of standard robust UIO.

The distributed fault diagnosis for nonlinear complex dynamical networks based on [formula omitted]-detection criterion

The actuator fault diagnosis problem of nonlinear complex dynamical networks with stochastic noise is investigated. Firstly, a local estimation based on a priori knowledge and measurement output is designed to estimate state for each node. A sufficient condition is derived such that the estimation error is stable and the estimation gain matrix is obtained in fault-free case by applying the stability theory. Secondly, considering additive actuator fault in presence of process and measurement noises, the residual signal based on estimation error is designed to detect faults and locate them. Thirdly, the threshold is acquired by applying the covariance matrix of estimation error. A fault detection and isolation logic is designed via the σ-detection criterion. Finally, two different examples are designed to verify the effectiveness of the designed actuator fault detection and isolation scheme.

Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM

Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM

Distributed Fault Diagnosis via Iterative Learning for Partial Differential Multi-Agent Systems with Actuators

Component failures can lead to performance degradation or even failure in multi-agent systems, thus necessitating the development of fault diagnosis methods. Addressing the distributed fault diagnosis problem in a class of partial differential multi-agent systems with actuators, a fault estimator is designed under the introduction of virtual faults to the agents. A P-type iterative learning control protocol is formulated based on the residual signals, aiming to adjust the introduced virtual faults. Through rigorous mathematical analysis utilizing contraction mapping and the Bellman–Gronwall lemma, sufficient conditions for the convergence of this protocol are derived. The results indicate that the learning protocol ensures the tracking of virtual faults to actual faults, thereby facilitating fault diagnosis for the systems. Finally, the effectiveness of the learning protocol is validated through numerical simulation.

A Novel Quality-Related Distributed Fault Diagnosis Framework for Large-Scale Sequential Manufacturing Processes

A Novel Quality-Related Distributed Fault Diagnosis Framework for Large-Scale Sequential Manufacturing Processes

A Two-Layer Distributed Fault Diagnosis Method Based on Correlation Feature Transfer for Large-Scale Sequential Process Industries

A Two-Layer Distributed Fault Diagnosis Method Based on Correlation Feature Transfer for Large-Scale Sequential Process Industries

A dissipativity-based robust fault diagnosis approach for clusters of small satellites

This study explores distributed fault diagnosis strategies for clusters of diverse small satellites, encompassing variations in Lipschitz-based non-linear dynamic systems and environmental factors. The investigation focuses on identifying rapid time-varying fault magnitudes and profiles across cluster agents interconnected by a directed graph. The analysis acknowledges uncertainties linked with state-coupled disturbances in the system model. Utilizing satellite communication topologies, a comprehensive augmented vector is formulated for individual satellites to diagnose both their states and faults, along with the states and faults of entire satellite clusters. Integrating an Unknown Input Observer (UIO) effectively untangles error dynamics from state-coupled disturbances. The UIO design is approached under two distinct conditions: one satisfying the case-matching criterion and the other that does not. The computation of observer gains employs the application of Linear Matrix Inequalities (LMIs). Furthermore, an adaptive robust fault estimator is proposed for scenarios where the matching condition is not met. This approach integrates dissipativity principles and is benchmarked against the H∞ framework. Simulation outcomes demonstrate the effectiveness of the proposed methodology. Specifically, the results show fault estimation across three clusters comprising seven small satellites. Notably, observers can be strategically positioned within select satellites rather than all, thereby mitigating the computational load for clusters with numerous satellites. This approach retains diagnostic efficacy while enhancing computational efficiency.

A Novel Distributed Fault Diagnosis Scheme Toward Open-Set Scenarios Based on Extreme Value Theory

Under closed-set scenarios (CSS), distributed modeling performs well in fault diagnosis of plant-wide industrial processes due to its flexibility and robustness. However, a more realistic scenario is often open, where unseen situations may arise unexpectedly, rendering existing methods infeasible. The advent of open-set recognition algorithms that can effectively distinguish known samples and reject unknown ones bridges this gap. Nevertheless, the poor scalability of these algorithms prevents them from being elegantly embedded in popular distributed modeling schemes, which hinders the implementation of plant-wide industrial process fault diagnosis toward open-set scenarios (OSS). In this work, we formulate a novel distributed fault diagnosis scheme toward OSS to solve this problem. Firstly, a mutual information-based local module decomposition and expansion strategy is proposed to minimize the loss of inter-module relevant information. Secondly, a novel generalized basic probability assignments generation technique based on extreme value theory is developed for modeling unknown information. It enables any classifier capable of probabilistic prediction to be applied to OSS and easily embedded in distributed modeling schemes. Finally, a conflict management scheme combining supervised and unsupervised is devised to address the vulnerability of the modified generalized combination rule to counter-intuitive results from fusing conflicting evidence. Experimental results on two plant-wide industrial process datasets demonstrate the proposed approach's feasibility and superiority.

A data‐driven distributed fault diagnosis scheme for large‐scale systems based on correlation analysis

AbstractThis paper studies data‐driven distributed fault diagnosis for large‐scale systems using sensor networks. To be specific, a distributed fault detection scheme based on correlation analysis is first proposed to improve the fault detection performance by minimizing the impact of noise‐induced uncertainty. The core of the method is to implement the correlation of the coupled nodes to reduce the covariance of the residual signal in a distributed manner. Then, a fault localization approach is developed to locate the fault by measuring and comparing the degree of abnormality. A case study on Tennessee Eastman process is given in the end to demonstrate the proposed approach.

Distributed diagnosis of discrete-event systems under dynamic event observations

In general, observable events are always assumed to be observed when studying the distributed diagnosis of discrete-event systems (DESs). However, this assumption may not be established in several DESs. In this paper, we address the problem of distributed fault diagnosis under dynamic event observations (DEOs) where some occurrences of observable events can not be captured, depending on the traces executed by systems. Then, the notion of distributed fault diagnosis under DEOs is formalized by introducing the notion of joint diagnosability with respect to DEOs (w.r.t. DEOs). For each local diagnoser, one algorithm for constructing communication models under DEOs is proposed. Finally, by constructing extended models above, an algorithm for testing joint diagnosability w.r.t. DEOs is presented.

Distributed fault diagnosis observer for multi-agent system against actuator and sensor faults

Distributed fault diagnosis observer for multi-agent system against actuator and sensor faults

A new belief rule base based distributed online fault diagnosis method for multi‐agent systems

AbstractIn this paper, based on belief rule base (BRB), we studied the distributed online fault diagnosis problem of multi‐agent systems (MASs). To begin with, considering the distributed nature of MASs, by using the relative information between an agent and its neighbors, a distributed online fault diagnosis model is constructed. Next, for the results of fault diagnosis, when an agent doesn't get enough relative information to diagnose the fault, the fault diagnosis result may be inaccurate, and if there exist too many neighbors of an agent, the number of attributes of the proposed BRB fault diagnosis model will be too large, which will lead to large computation. To improve the performance of the proposed distributed online fault diagnosis model, a real‐time communication topology switching strategy was proposed. Under the constructed distributed fault diagnosis model, the real‐time performance and the accuracy of fault diagnosis for agents can be guaranteed simultaneously. Finally, a simulation is given to validate the effectiveness and advancement of the distributed online fault diagnosis method proposed in this paper.

Dynamic Offloading Loading Optimization in Distributed Fault Diagnosis System with Deep Reinforcement Learning Approach

Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided in this paper, which can effectively address the problems of task processing delays and enhanced computational complexity. As the resources at the MEC and intelligent terminals are limited, performing reasonable resource allocation optimization can improve the performance, especially for a multi-terminals offloading system. In this study, to minimize the task computation delay, we jointly optimize the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection under a dynamic environment with stochastic task arrivals. The challenging dynamic joint optimization problem is formulated as a reinforcement learning (RL) problem, which is designed as the computational offloading policies to minimize the long-term average delay cost. Two deep RL strategies, deep Q-learning network (DQN) and deep deterministic policy gradient (DDPG), are adopted to learn the computational offloading policies adaptively and efficiently. The proposed DQN strategy takes the MEC selection as a unique action while using the convex optimization approach to obtain the local content splitting ratio and the transmission/computation power allocation. Simultaneously, the actions of the DDPG strategy are selected as all dynamic variables, including the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection. Numerical results demonstrate that both proposed strategies perform better than the traditional non-learning schemes. The DDPG strategy outperforms the DQN strategy in all simulation cases exhibiting minimal task computation delay due to its ability to learn all variables online.

Event-Triggered Distributed Attack Detection and Fault Diagnosis

This article investigates the security issue for multi-agent systems (MASs). Due to the effect of physical faults and cyber attacks, MASs are prone to anomalies. To meet these challenges, an observer-based distributed attack detection and fault diagnosis scheme is proposed. Firstly, in order to reduce unnecessary communication loss, an event-triggered mechanism is designed for each agent to determine whether to transmit the current measurement to its own state estimator and its neighbors. Then, based on the information received from neighbors, each agent designs a bank of attack detectors based on unknown input observer for the neighbors to detect whether there are attacks in the communication with the neighbor agents. In addition, for each agent, according to the local measurement and the estimated states of adjacent agents, a fault diagnoser based on Luenberger observer is designed, which can be combined with the attack detectors to make the distributed fault diagnosis for each agent. Finally, the effectiveness and scalability of the proposed scheme are verified by applying it to two smart grids with multiple generators.

A New Topology-Switching Strategy for Fault Diagnosis of Multi-Agent Systems Based on Belief Rule Base.

Effective fault-diagnosis strategies have been the focus of research on multi-agent systems (MASs). In this paper, the belief rule base (BRB)-based distributed fault-diagnosis problem for MASs is investigated, and a topology-switching strategy is developed to increase the reliability of fault-diagnosis model. Firstly, a BRB-based distributed fault-diagnosis model is constructed for the MAS with multiple faults, then expert knowledge is used to judge whether the agent is faulty. Then, considering that the system may be influenced by the fault or some other factors and thus leading to a decrease in the accuracy of the fault-diagnosis results, a topology-switching strategy based on the average distance of the output diagnosis accuracy is proposed to update the topology of the agent so that the fault-diagnosis results can be more reliable. Note that the topology-switching threshold is designed based on the average distance between the accuracy of the fault diagnosis of each agent. The method proposed in this paper can solve the problem when the fault-diagnosis accuracy of the model is affected by some common factors and thus decreases, and can improve the reliability of the fault-diagnosis model very well. Finally, the effectiveness of the BRB-based distributed fault-diagnosis model and the proposed topology-switching strategy to improve the fault-diagnosis accuracy is verified by simulation examples.

Distributed Knowledge Inference Framework for Intelligent Fault Diagnosis in IIoT Systems

Knowledge inference using knowledge graphs is one of the recent research trends for fault diagnosis. As complex Industrial Internet of Things (IIoT) systems evolve to incorporate distributed subsystems, each subsystem may have local measurements interpreted by a local Knowledge Base (KB). In this work, we propose a distributed fault diagnosis framework with distributed path-based reasoning. In contrast to centralized reasoning that requires merging of the distributed KBs, the proposed framework employs a centralized coordinator to transfer the model among decentralized agents and allows an agent to handover training paths for others to continue training by filling in missing links using their local KBs. Our work is the very first distributed knowledge inference framework to reason among distributed fault diagnosis systems to tackle the KB isolation, scalability and flexibility issues of centralized fault diagnosis systems. To validate our framework with IIoT datasets, we build a fault knowledge graph from the Tennessee Eastman (TE) process simulations. Evaluations show that our framework outperforms the non-cooperative distributed reasoning method in terms of prediction accuracy. With the TE process dataset, the mean reciprocal rank is improved from 0.1612 to 0.19 with transferred models, and further to 0.206 with hand-over queries between agents.